The recent developments in the field of display technologies have seen great diversity in display sizes and same content is required to be displayed in different dimensions and aspect ratio for different devices. Typically, videos recorded for the old 4:3 ratio of CRT television are now displayed on 16:9 wide screen TV.
There is thus a need of algorithm that could adapt images to displays different than originally intended for.
Basic image resizing techniques are linear scaling or cropping. However, these techniques lead to image quality degradation due to loss of details, anisotropic squish or stretch, suppression of region outside the cropping window, etc.
Hence effective adaptation of images considering the image content is needed. Such an intelligent adaptation is known in the art as “Image retargeting” or “Video retargeting” if video is considered.
For modifying “intelligently” an image, numerous methods use a saliency map which defines an information value for each pixel.
For instance, document EP 1 968 008 discloses a method for content-aware image retargeting which is known as “Seam Carving”. A saliency map, also called an energy image, from a source image is generated according to an energy function, often a luminance gradient function. From the energy image, one or more seams are determined according to a minimizing function such that each seam has a minimal energy. Each seam is applied to the source image by suppressing or duplicating the seam to obtain a target image that preserves content but with a different aspect ratio.
In this document, a seam is a contiguous path of pixels going through the image from one side to the opposite side.
This technique was extended to video retargeting by defining a 2D seam surface in a 3D video space-time cube. The intersection of the surface with each frame defines a seam in the sense of the document. The manifold seam surface allows the seam to change adaptively over time, maintaining temporal coherence.